Use of artificial neuralnetwork for modeling of pollutent

Artificial Neural Networks (ANN) is implemented for predicting air quality. The models, in general, could predict air quality patterns with modest accuracy However, ANNM model performed extremely well in comparison to other models for predicting annual data as well as daily data. Industry emits small amounts of nitrogen oxides to the environment. As the regulatory authorities demand the reduction of the resulting air pollution, existing plants are looking for economical ways to comply with this demand. Several Artificial Neural Networks models were trained from several months of operating plant data to predict the NOx concentration in the tail gas, and their total amount emitted the environment. This paper describes the development of artificial neural network-based vehicular exhaust emission & industrial models for predicting carbon monoxide concentrations at air quality control regions in the city of Raipur, India, viz. a typical traffic intersection.Which can work with limited number of data sets and are robust enough to handle data with noise and errors.The Artificial Neural Networks models gave small errors, 0.6% relative error on the nitrogen oxides concentration prediction. Thenitrogen oxides emission rate, especially the beneficial effect of cooling the absorbed gas and reticulating liquids in the absorption towers

Exploiting Neuron and Synapse Filter Dynamics in Spatial Temporal Learning of Deep Spiking Neural Network

The recent discovered spatial-temporal information processing capability of bio-inspired Spiking neural networks (SNN) has enabled some interesting models and applications. However designing large-scale and high-performance model is yet a challenge due to the lack of robust training algorithms. A bio-plausible SNN model with spatial-temporal property is a complex dynamic system. Each synapse and neuron behave as filters capable of preserving temporal information. As such neuron dynamics and filter effects are ignored in existing training algorithms, the SNN downgrades into a memoryless system and loses the ability of temporal signal processing. Furthermore, spike timing plays an important role in information representation, but conventional rate-based spike coding models only consider spike trains statistically, and discard information carried by its temporal structures. To address the above issues, and exploit the temporal dynamics of SNNs, we formulate SNN as a network of infinite impulse response (IIR) filters with neuron nonlinearity. We proposed a training algorithm that is capable to learn spatial-temporal patterns by searching for the optimal synapse filter kernels and weights. The proposed model and training algorithm are applied to construct associative memories and classifiers for synthetic and public datasets including MNIST, NMNIST, DVS 128 etc.; and their accuracy outperforms state-of-art approaches

Audio Effects Emulation with Neural Networks

[EN] This paper discusses if using Neural Networks we can develop model which emulates audio effects and also if it can stand up to traditional audio effect emulators. This report includes the comparison of the performance between Recurrent Neural Networks such as Long Short Term Memory and Gated Recurrent Unit, and also Convolutional Neural Networks. This paper also checks if the best performing network, dealing with a online stream of inputs, can produce its outputs without a significant delay, as the ones of traditional audio effect emulators. The networks were trained to emulate an EQ effect. The results compared the audio produced by the network with the audio we want the network to produce, which is the audio modified by the EQ. These results were compared quantitatively, calculating the absolute difference between the two audio and comparing the frequency spectrum; and qualitatively, checking if people could hear both audios as the same one. Long Short Term Memory turned out to be the ones which achieved the best results. However, they could not produce a stream of outputs without a significant delay nor an acceptable error.Del Tejo Catala, O. (2017). Audio Effects Emulation with Neural Networks. http://hdl.handle.net/10251/88860.TFG

A Reinforcement Learning Framework for Spiking Networks with Dynamic Synapses

An integration of both the Hebbian-based and reinforcement learning (RL) rules is presented for dynamic synapses. The proposed framework permits the Hebbian rule to update the hidden synaptic model parameters regulating the synaptic response rather than the synaptic weights. This is performed using both the value and the sign of the temporal difference in the reward signal after each trial. Applying this framework, a spiking network with spike-timing-dependent synapses is tested to learn the exclusive-OR computation on a temporally coded basis. Reward values are calculated with the distance between the output spike train of the network and a reference target one. Results show that the network is able to capture the required dynamics and that the proposed framework can reveal indeed an integrated version of Hebbian and RL. The proposed framework is tractable and less computationally expensive. The framework is applicable to a wide class of synaptic models and is not restricted to the used neural representation. This generality, along with the reported results, supports adopting the introduced approach to benefit from the biologically plausible synaptic models in a wide range of intuitive signal processing

Pattern recognition in software engineering trend adapting

Whether and when to adapt to certain software engineering trends are difficult questions to be answered by many decision-makers. The main reasons are due to the fact that evolution of software engineering trends itself is determined by various factors, many of which come from the fields outside of the software technology, thus hard to predict. So it is even harder to estimate the cost and benefit when adapting to certain trends. This paper is intended to study ways to decrease the risk involved in such decision making processes, by developing a pattern from past software engineering trends. While the pattern cannot answer all the questions by itself, it can relief the decision makers in a large extent by providing the most important information relevant to the software engineering trends. The pattern recognition is achieved by using neural networks. Our result seems to be very encouraging, which begins to prove that there does exist pattern between the input data that we can observe and the output data that we need to know. Although more trends need to be observed and analyzed before we can reach a more concrete conclusion, it does show that neural network may be a valid approach in future research

Differentiable Artificial Reverberation

Artificial reverberation (AR) models play a central role in various audio applications. Therefore, estimating the AR model parameters (ARPs) of a target reverberation is a crucial task. Although a few recent deep-learning-based approaches have shown promising performance, their non-end-to-end training scheme prevents them from fully exploiting the potential of deep neural networks. This motivates to introduce differentiable artificial reverberation (DAR) models which allows loss gradients to be back-propagated end-to-end. However, implementing the AR models with their difference equations "as is" in the deep-learning framework severely bottlenecks the training speed when executed with a parallel processor like GPU due to their infinite impulse response (IIR) components. We tackle this problem by replacing the IIR filters with finite impulse response (FIR) approximations with the frequency-sampling method (FSM). Using the FSM, we implement three DAR models -- differentiable Filtered Velvet Noise (FVN), Advanced Filtered Velvet Noise (AFVN), and Feedback Delay Network (FDN). For each AR model, we train its ARP estimation networks for analysis-synthesis (RIR-to-ARP) and blind estimation (reverberant-speech-to-ARP) task in an end-to-end manner with its DAR model counterpart. Experiment results show that the proposed method achieves consistent performance improvement over the non-end-to-end approaches in both objective metrics and subjective listening test results.Comment: Manuscript submitted to TASL